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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

The minimal residual QR-factorization algorithm for reliably solving subset regression problems

A new algorithm to solve test subset regression problems is described, called the minimal residual QR factorization algorithm (MRQR). This scheme performs a QR factorization with a new column pivoting strategy. Basically, this strategy is based on the change in the residual of the least squares problem. Furthermore, it is demonstrated that this basic scheme might be extended in a numerically efficient way to combine the advantages of existing numerical procedures, such as the singular value decomposition, with those of more classical statistical procedures, such as stepwise regression. This extension is presented as an advisory expert system that guides the user in solving the subset regression problem. The advantages of the new procedure are highlighted by a numerical example.

Verhaegen, M. H.↗
NASA NTRS1989

Cloud model-based simulations of satellite microwave data and their application to an SSM/I rain algorithm

Satellite microwave observations of convective rain systems are simulated using a three-dimensional cloud model and a microwave radiative-transfer model. The simulated data sets are then used as a basis to derive rain estimation algorithms which are tested on actual satellite data. A tropical simulation is discussed, and the algorithms are applied to SSM/I data.

Adler, Robert F.↗
NASA NTRS1992

Tests of a simple data merging algorithm for the GONG project

The GONG (Global Oscillation Network Group) project proposes to reduce the impact of diurnal variations on helioseismic measurements by making long-term observations of solar images from six sites placed around the globe. The sun will be observed nearly constantly for three years, resulting in the acquisition of l+ terabyte of image data. To use the solar network to maximum advantage, the images from the sites must be combined into a single time series to determine mode frequencies, amplitudes, and line widths. Initial versions of combined, i.e., merged, time series were made using a simple weighted average of data from different sites taken simultaneously. In order to accurately assess the impact of the data merge on the helioseismic measurements, a set of artificial solar disk images was made using a standard solar model and containing a well known set of oscillation modes and frequencies. This undegraded data set and data products computed from it were used to judge the relative merits of various data merging schemes. The artificial solar disk images were subjected to various instrumental and atmospheric degradations, dependent on site and time, in order to create a set of images simulating those likely to be taken at the site. The degraded artificial solar disk images for the six observing sites were combined in various ways to form merged time series of images and mode coefficients. Various forms of a weighted average were used, including an equally-weighted average, an average with weights dependent upon air mass and averages with weights dependent on various quality assurance parameters. Both the undegraded solar disk image time series and several time series made up of various combinations of the degraded solar disk images from the six sites were subjected to standard helioseismic measurement processing. This processing consisted of coordinate remapping, detrending, spherical harmonic transformation, computation of power series for the oscillation mode coefficients, and mode frequency identification. Visual and statistical evaluation of the merged data sets themselves and differences between the merged and undegraded data set shows good agreement between the two data sets. Some slight differences in image scale and registration appear between the undegraded data set and the various merged data sets. In the set of power series made from the mode coefficients of the merged data sets, some power leakage is observed into the background and into slightly lower l-value modes, especially at higher l-value mode frequencies. The results of the comparison of time series and mode oscillation frequencies of the undegraded data with those of the data merged using weighted averages indicate that, at least for p-mode solar oscillations, a weighted average of either the detrended remapped images or the mode coefficients gives good determination of the mode frequencies and adequate-to-good determination of the amplitudes and widths of the mode frequency lines. This conclusion is most advantageous to the analysis of the massive amounts of data to be received by the GONG network.

Williams, W. E.↗
NASA NTRS1996

Experimental Optimization Methods for Multi-Element Airfoils

A modern three element airfoil model with a remotely activated flap was used to investigate optimum flap testing position using an automated optimization algorithm in wind tunnel tests. Detailed results for lift coefficient versus flap vertical and horizontal position are presented for two angles of attack: 8 and 14 degrees. An on-line first order optimizer is demonstrated which automatically seeks the optimum lift as a function of flap position. Future work with off-line optimization techniques is introduced and aerodynamic hysteresis effects due to flap movement with flow on are discussed.

Landman, Drew↗
NASA NTRS2002

Advanced Guidance and Control Methods for Reusable Launch Vehicles: Test Results

There are a number of approaches to advanced guidance and control (AG&C) that have the potential for achieving the goals of significantly increasing reusable launch vehicle (RLV) safety/reliability and reducing the cost. In this paper, we examine some of these methods and compare the results. We briefly introduce the various methods under test, list the test cases used to demonstrate that the desired results are achieved, show an automated test scoring method that greatly reduces the evaluation effort required, and display results of the tests. Results are shown for the algorithms that have entered testing so far.

Hanson, John M.↗
NASA NTRS2004

A Portable Electronic Nose For Hydrazine and Monomethyl Hydrazine Detection

The Space Program and military use large quantities Hydrazine (Hz) and monomethyl hydrazine (MMI-I) as rocket propellant. These substances are very toxic and are suspected human carcinogens. The American Conference of Governmental Industrial Hygienist set the threshold limit value to be 10 parts per billion (ppb). Current off-the-shelf portable instruments require 10 to 20 minutes of exposure to detect 10 ppb concentration. This shortcofriing is not acceptable for many operations. A new prototype instrument using a gas sensor array and pattern recognition software technology (i.e., an electronic nose) has demonstrated the ability to identify either Hz or MM}{ and quantify their concentrations at 10 parts per billion in 90 seconds. This paper describes the design of the portable electronic nose (e-nose) instrument, test equipment setup, test protocol, pattern recognition algorithm, concentration estimation method, and laboratory test results.

Young, Rebecca C.↗
NASA NTRS2017

An Analysis of the Speed Commands from an Interval Management Algorithm during the ATD-1 Flight Test

NASA's first Air Traffic Management Technology Demonstration (ATD-1) successfully completed a nineteen-day flight test under a NASA contract with Boeing, with Honeywell and United Airlines as sub-contractors. An Interval Management (IM) avionics prototype was built based on international IM standards, integrated into two test aircraft, and then flown in real-world conditions to determine if the goals of improving aircraft efficiency and airport throughput during high-density arrival operations could be met. This paper describes the speed behavior of the IM avionics prototype, focusing on the speed command rate and the number of speed increases.

Watters, Christine↗
NASA NTRS1972

Design of automata theory of cubical complexes with applications to diagnosis and algorithmic description

The following problems are considered: (1) methods for development of logic design together with algorithms, so that it is possible to compute a test for any failure in the logic design, if such a test exists, and developing algorithms and heuristics for the purpose of minimizing the computation for tests; and (2) a method of design of logic for ultra LSI (large scale integration). It was discovered that the so-called quantum calculus can be extended to render it possible: (1) to describe the functional behavior of a mechanism component by component, and (2) to compute tests for failures, in the mechanism, using the diagnosis algorithm. The development of an algorithm for the multioutput two-level minimization problem is presented and the program MIN 360 was written for this algorithm. The program has options of mode (exact minimum or various approximations), cost function, cost bound, etc., providing flexibility.

Roth, J. P.↗
DOE OSTI2023

Sparse Symmetric Format for Tucker Decomposition

Tensor-based methods are receiving renewed attention in recent years due to their prevalence in diverse real-world applications. There is considerable literature on tensor representations and algorithms for tensor decompositions, both for dense and sparse tensors. Many applications in hypergraph analytics, machine learning, psychometry, and signal processing result in tensors that are both sparse and symmetric, making them an important class for further study. Similar to the critical Tensor Times Matrix chain operation (TTM c ) in general sparse tensors, the $\underline{S}$ parse $\underline{S}$ ymmetric $\underline{T}$ ensor $\underline{T}$ imes $\underline{S}$ ame $\underline{M}$ atrix $\underline{c}$ hain (S 3 TTM c ) operation is compute and memory intensive due to high tensor order and the associated factorial explosion in the number of non-zeros. We present the novel Compressed Sparse Symmetric (CSS) format for sparse symmetric tensors, along with an efficient parallel algorithm for the S 3 TTM c operation. We theoretically establish that S 3 TTM c on CSS achieves a better memory versus run-time trade-off compared to state-of-the-art implementations, and visualize the variation of the performance gap over the parameter space. We demonstrate experimental findings that confirm these results and achieve up to 2.72× speedup on synthetic and real datasets. The scaling of the algorithm on different test architectures is also showcased to highlight the effect of machine characteristics on algorithm performance.

42 ENGINEERING↗
NASA NTRS2024

A Multi‐Probe Automated Classification of Ice Crystal Habits During the IMPACTS Campaign

Although all ice crystals are unique, many can be grouped together by shape or habit, with members of a habit class sharing similar representations of properties such as fall velocity and growth rate. A decision tree algorithm designed to be adaptable to any particle imaging probe, thus enabling the creation of habit size distributions over a size range larger than that of any probe on its own, is used to classify ice crystals imaged by three airborne cloud probes in mid-latitude winter cyclones during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Crystals are sorted into seven habit classes based on their morphological properties: sphere, column/needle, plate, graupel, dendrite, aggregate, and irregular. Although adaptability was its primary goal, the algorithm was found to be moderately skillful for identifying idealized habit images. Quantitative tests of the algorithm’s adaptability displayed mixed results, as Two-Dimensional Stereo Probe (2DS) classifications showed moderate correlation with Particle Habit Imaging and Polar Scattering Probe (PHIPS) classifications, but only weak correlation with High Volume Precipitation Spectrometer (HVPS) classifications. The algorithm was applied to random sets of images from each probe in a case study of a mesoscale snow band sampled on 7 February 2020. In the case study, qualitative analysis of particle images revealed general agreement on classifications among the probes, supporting the algorithm’s applicability to multiple cloud probes. Most classifications appeared correct upon manual inspection, suggesting that in practical use, the algorithm is reasonably able to classify non-idealized images.

Julian Schima↗
DOE OSTI2024

Model-Based Detection of Coordinated Attacks (DCA) in Distribution Systems

The fast-paced growth in digitization of smart grid components enhances system observability and remote-control capabilities through efficient communication. However, enhanced connectivity results in heightened system vulnerability towards cybersecurity risks in the cyber-physical power system. Coordinated cyber-attacks (CCA), when undetected, lead to system-wide impact in terms of large disturbances or widespread outages. Detecting CCA in the cyber layer is critical to thwart cyber-attacks in real-time before the attack impacts the physical system. The challenge of locating CCA stems from the complex grid dynamics, making it difficult to distinguish between normal operational variations and cyber-attack impact. CCA often employs multiple attack vectors targeting geographically distributed components, further complicating CCA identification. Existing research in intrusion detection is primarily focused on the transmission network and limited to detecting individual attacks. In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. The solution includes understanding the system model, learning the system dynamics, and correlating individual cyber-attacks to extract the attacker’s objective. The developed learning algorithm identifies the most probable attack path to reach the attacker’s objective by predicting the next attack steps. A DNP3-based cyber-physical co-simulation testbed is developed to test the proposed algorithm using the IEEE 13-node test feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2019

Sensor Analysis, Modeling, and Test for Robust Propulsion System Autonomy

An approach is presented supporting analysis, modeling, and test validation of operational flight instrumentation (OFI) that facilitates critical functions for the Space Launch System (SLS) main propulsion system (MPS). Certain types of OFI sensors were shown to exhibit highly nonlinear and non-gaussian noise characteristics during acceptance testing, motivating the development of advanced modeling and simulation (M&S) capability to support algorithm verification and flight certification. Hardware model and algorithm simulation fidelity was informed by a risk scoring metric; redesign of high-risk algorithms using test-validated sensor models significantly improved their expected performance as evaluated using Monte Carlo acceptance sampling methods. Autonomous functions include closed-loop ullage pressure regulation, pressurant leak detection, and fault isolation for automated safing and crew caution and warning (C&W).

Orr, Jeb S.↗
NASA NTRS2004

Adaptive Controls Method Demonstrated for the Active Suppression of Instabilities in Engine Combustors

An adaptive feedback control method was demonstrated that suppresses thermoacoustic instabilities in a liquid-fueled combustor of a type used in aircraft engines. Extensive research has been done to develop lean-burning (low fuel-to-air ratio) combustors that can reduce emissions throughout the mission cycle to reduce the environmental impact of aerospace propulsion systems. However, these lean-burning combustors are susceptible to thermoacoustic instabilities (high-frequency pressure waves), which can fatigue combustor components and even the downstream turbine blades. This can significantly decrease the safe operating lives of the combustor and turbine. Thus, suppressing the thermoacoustic combustor instabilities is an enabling technology for lean, low-emissions combustors under NASA's Propulsion and Power Program. This control methodology has been developed and tested in a partnership of the NASA Glenn Research Center, Pratt & Whitney, United Technologies Research Center, and the Georgia Institute of Technology. Initial combustor rig testing of the controls algorithm was completed during 2002. Subsequently, the test results were analyzed and improvements to the method were incorporated in 2003, which culminated in the final status of this controls algorithm. This control methodology is based on adaptive phase shifting. The combustor pressure oscillations are sensed and phase shifted, and a high-frequency fuel valve is actuated to put pressure oscillations into the combustor to cancel pressure oscillations produced by the instability.

Kopasakis, George↗
DOE OSTI2021

A Heterogeneous System for Eagle Detection, Deterrent, and Wildlife Collision Detection for Wind Turbines (Final Technical Report)

This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.

17 WIND ENERGY↗
DOE OSTI2020

A conservative phase-space moving-grid strategy for a 1D-2V Vlasov–Fokker–Planck Solver

In this work, we develop a conservative configuration- and velocity-space (i.e., phase-space) moving-grid strategy for the Vlasov–Fokker–Planck (VFP) equation in a planar geometry. The velocity-space grid is normalized and shifted in terms of the thermal speed and the bulk-fluid velocity, respectively. The configuration-space grid is moved according to a mesh-motion-partial-differential equation (MMPDE), which equidistributes a monitor function that is inversely proportional to the gradient-length scales of the macroscopic plasma quantities. The resulting inertial terms in the transformed VFP equations are discretized to ensure the discrete conservation of mass, momentum, and energy. To satisfy the discrete conservation theorems in the presence of phase-space mesh motion, we employ the method of discrete nonlinear constraints – explored in previous studies – but the underlying symmetries are determined in a much more efficient manner than before. The conservative grid-adaptivity strategy provides an efficient scheme that resolves important physical structures in the phase-space while controlling the computational complexity at all times. We demonstrate the favorable features of the proposed algorithm through a set of test cases of increasing complexity. The problems test independent components of the algorithms, as well as the integrated capability on settings relevant to inertial confinement fusion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗